Hybrid quantum-classical neural networks represent a promising frontier in the search for improved machine learning models. This thesis explores the integration of quantum layers within classical convolutional neural network architectures, aiming to leverage quantum entanglement and feature mapping to enhance learning capabilities. A detailed methodology for constructing and training such hybrid models is presented, using PyTorch and Qiskit Machine Learning frameworks. Experiments investigate the performance impact of inserting quantum layers at different stages of the neural network pipeline. The results suggest that quantum components can introduce meaningful transformations even with a limited number of qubits, motivating further research into scalable quantum machine learning. The full implementation is made publicly available, and future work will focus on expanding experimental evaluations and publishing additional findings.